This NRSA Postdoctoral Training grant application will enhance knowledge of the diffusion of school-based mental health service interventions by examining the effects of social networks on teachers' adoption of key intervention components. Specifically, in this proposal, I combine theories from mental health services research, diffusion of innovations research, and social networks research to: (a) provide a detailed description of the structure of urban elementary school teachers' advice networks (b) test the effects of these advice networks on teachers' adoption of core components of a school-based mental health intervention and (c) use network data to develop a refined model for the identification of influential teachers who can help spread intervention practices in future models of school-based mental health service delivery. The proposed research will use data collected within the context of an ongoing NIMH-funded study of a school-based mental health intervention. Demographic data and data on three types of teacher advice networks (i.e., behavioral, involving families, and instructional) will be collected from approximately 177 kindergarten through eighth grade teachers in six urban elementary schools. Data on teacher adoption of four intervention components (i.e., the Good Behavior Game, the Daily Report Card, the Good News Note, and Classwide Peer Tutoring) and teacher-led classroom interactions will be collected from an estimated subset of 61 kindergarten through fourth grade teachers targeted to adopt intervention components. Social network analysis will be used to determine levels of density, reciprocity, and demographic homophily in the three types of teacher advice networks. Mixed models will be used to test the effects of advice networks on teachers' time of adoption and frequency of use of the four intervention components. Results from these analyses will be used to build a new model for selecting influential teachers to help spread intervention components among their peers. [unreadable] [unreadable] [unreadable]